Automatic feature extraction from a relational database

ABSTRACT

Techniques facilitating automatic feature extraction from a relational database are provided. In an embodiment, a method can include generating an entity graph based on a relational database, wherein the entity graph comprises a first node associated with a first table in the relational database and a second node associated with a second table in the relational database. In another embodiment, the method can include joining the first table and the second table based on an edge between the first table and the second table defined by the entity graph, wherein a resulting joined table is connected by a column of data. In another embodiment, the method can include extracting a feature from the column of data using a data mining algorithm selected from a set of data mining algorithms based on a type of data in the column of data.

BACKGROUND

The subject disclosure relates to feature engineering, and morespecifically to automatically extracting features from a relationaldatabase for predictive modeling.

SUMMARY

The following presents a summary to provide a basic understanding of oneor more embodiments of the invention. This summary is not intended toidentify key or critical elements, or delineate any scope of theparticular embodiments or any scope of the claims. Its sole purpose isto present concepts in a simplified form as a prelude to the moredetailed description that is presented later. In one or more embodimentsdescribed herein, systems, computer-implemented methods, apparatusand/or computer program products that facilitate synchronization ofprocessing components for semantic labeling are described.

According to an embodiment, a computer implemented method can includegenerating, by a device coupled to a processing unit, an entity graphthat based on a relational database, where the entity graph comprises afirst node associated with a first table in the relational database anda second node associated with a second table in the relational database.The computer implemented method can also include joining, by the device,the first table and the second table based on an edge between the firsttable and the second table defined by the entity graph, where aresulting joined table is connected by a column of data. The computerimplemented method can also include extracting, by the device, a featurefrom the column of data using a data mining algorithm selected from aset of data mining algorithms based on a type of data in the column ofdata.

In another embodiment, the computer-implemented method can includeselecting, by the device, the data mining algorithm from the set of datamining algorithms based on determining whether the data is from a groupconsisting of spatial-temporal data, time-series data, sequence data,item set data, number set data, singleton data, text data and imagedata.

According to an embodiment, a system can include a memory that storescomputer executable components and a processor that executes thecomputer executable components stored in the memory. The computerexecutable components can include a graphing component that can generatea graph based on a relational database, wherein the graph has a set ofnodes that correspond to respective tables in the relational database.The computer executable components can also include a joining componentthat can join the respective tables to form a joined table based on anedge connecting nodes of the set of nodes, wherein the joined table isjoined by a column of data that is shared by the respective tables. Thecomputer executable components can also include a feature extractioncomponent that can extract a feature from the column of data using adata mining algorithm that is selected from a set of data miningalgorithms based on a type of data in the column of data.

According to yet another embodiment, a computer implemented method caninclude cleaning, by a device operatively coupled to a processing unit,a relational database by filling in missing values in incomplete dataand removing broken data. The computer implemented method can alsoinclude generating, by the device, using a relational database, anentity graph with a first node and a second node, wherein the first nodecorresponds to a first table and the second node corresponds to a secondtable, and wherein the first table and the second table have respectivecolumns that are related to each other. The computer implemented methodcan also include joining, by the device, the first table and the secondtable at the respective columns that are related to each other andforming a joined column of data. The computer implemented method canalso include extracting, by the device, a feature from the joined columnof data using a data mining algorithm selected from a set of data miningalgorithms based on a type of data in the joined column of data.

According to yet another embodiment, a system can include a memory thatstores computer executable instructions and a processor that executesthat computer executable instructions to perform operations. Theoperations can include generating an entity graph based on a relationaldatabase, wherein the entity graph comprises a first node associatedwith a first table in the relational database and a second nodeassociated with a second table in the relational database. Theoperations can also include joining the first table and the second tablebased on an edge between the first table and the second table defined bythe entity graph, wherein a resulting joined table is connected by acolumn of data. The operations can also include determining a type ofthe data in the column of data and selecting a data mining algorithmfrom a set of data mining algorithms based on the type of data in thecolumn of data. The operations can also include extracting a featurefrom the column of data using the data mining algorithm.

According to yet another embodiment, computer program product can beprovided to facilitate automatic feature extraction, the computerprogram product comprising a computer readable storage medium havingprogram instructions embodied therewith, the program instructionsexecutable by a processing component to cause the processing componentto generate an entity graph based on a relational database, wherein theentity graph comprises a first node associated with a first table in therelational database and a second node associated with a second table inthe relational database. The processing component can also receive atarget variable based on a user input and join the first table and thesecond table based on an edge between the first table and the secondtable defined by the entity graph, wherein a resulting joined table isconnected by a column of data and the column of data is associated withthe target variable. The processing component can also extract a featurefrom the column of data using a data mining algorithm selected from aset of data mining algorithms based on a type of data in the column ofdata.

DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example, non-limiting flowgraph representing a feature extraction system in accordance with one ormore embodiments described herein.

FIG. 2 illustrates another block diagram of an example, non-limitingfeature extraction system that in accordance with one or moreembodiments described herein.

FIG. 3 illustrates another block diagram of an example, non-limitingfeature extraction system that identifies a type of data and selects anappropriate data mining technique in accordance with one or moreembodiments described herein.

FIG. 4 illustrates another block diagram of an example, non-limitingfeature extraction system that collects extracted features across arelational database in accordance with one or more embodiments describedherein.

FIG. 5 illustrates another block diagram of an example, non-limitingfeature extraction system that interfaces to receive new data miningalgorithms in accordance with one or more embodiments described herein.

FIG. 6 illustrates a flow diagram of an example, non-limitingcomputer-implemented method that facilitates automatic featureextraction in accordance with one or more embodiments described herein.

FIG. 7 illustrates a flow diagram of another example, non-limitingcomputer-implemented method that facilitates automatic featureextraction in accordance with one or more embodiments described herein.

FIG. 8 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated.

FIG. 9 illustrates a block diagram of an example, non-limiting cloudcomputing environment in accordance with one or more embodiments of thepresent invention.

FIG. 10 illustrates a block diagram of example, non-limiting abstractionmodel layers in accordance with one or more embodiments of the presentinvention.

DETAILED DESCRIPTION

The following detailed description is merely illustrative and is notintended to limit embodiments and/or application or uses of embodiments.Furthermore, there is no intention to be bound by any expressed orimplied information presented in the preceding Background or Summarysections, or in the Detailed Description section.

One or more embodiments are now described with reference to thedrawings, wherein like referenced numerals are used to refer to likeelements throughout. In the following description, for purposes ofexplanation, numerous specific details are set forth in order to providea more thorough understanding of the one or more embodiments. It isevident, however, in various cases, that the one or more embodiments canbe practiced without these specific details.

It is to be understood that although this disclosure includes a detaileddescription on cloud computing, implementation of the teachings recitedherein are not limited to a cloud computing environment. Rather,embodiments of the present invention are capable of being implemented inconjunction with any other type of computing environment now known orlater developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g., networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported, providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (IaaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure that includes anetwork of interconnected nodes.

In various embodiments disclosed herein, provided is a system that canautomatically extract features from a relational database. The featureextraction system can first generate an entity graph from the relationdatabase, where nodes on the graph are tables, and edges definerelations between the tables. The feature extraction system can thenjoin two or more tables based on the relationships between the tablesand a resulting joined table can be connected by a column of data. Oneor more features can be extracted from the column of data using a datamining algorithm that can be selected based on the determined type ofdata in the column of data. A collector can then collect each of thefeatures extracted by traversing the graph to a predetermined depth.

Typically feature engineering can be a bottleneck during predictiveanalytics projects, where the feature engineering can take up to 80% ofthe total effort. Feature engineering is the process of using domainknowledge of the data to create features that make machine learningalgorithms work. Faster, and automatic feature engineering can alleviatethis bottleneck and reduce the workload of data scientists whenimplementing predictive solutions.

Turning now to FIG. 1 illustrated is a block diagram of an example,non-limiting flow graph 100 representing an automatic feature extractionsystem in accordance with one or more embodiments described herein.

The system can receive as an input a relational database 102. Arelational database can include a set of tables containing data fittedinto predefined categories. Each table (which can be referred to as a“relation”) can contain one or more data categories in columns. Each rowcan contains a unique instance of data for the categories defined by thecolumns. For example, a typical business order entry database wouldinclude a table that described a customer with columns for name,address, phone number, and so forth. Another table would describe anorder: product, customer, date, sales price, and so forth. Predictivemodeling can make predictions about one or more events using the data inthe relational database, but correctly understanding and interpretingrelationships between the data is important.

For instance, in one embodiment, relational database 102 can includetables showing consumers profile information including income, age,location and education as well as tables showing banking transactions,transaction amounts, types of purchased products, location of shops,etc. In order to make predictions using the data in the relationaldatabase, a predictive model uses features as input for the predictivemodel. In the example described here, features can include elements ofthe data that are relevant to the desired prediction. For instance, ifthe predictive model was designed to predict a likelihood of a consumerapplying for a credit card, determining which data is most relevant willassist in improving the accuracy of the prediction.

The system can generate an entity graph 104 from the relational databasewhere the entity graph 104 comprises a first node associated with afirst table in the relational database and a second node associated witha second table in the relational database. In an embodiment, therelational database can be first cleaned by the system. Cleaning thedata can involve standardizing a format of data in the relationaldatabase and filling in missing values while also removing broken data.In an embodiment, sensitive data that may identify individual entitiescan be anonymized. In other embodiments, the data can be sampled inorder to reduce the file size.

Once the data is cleaned, the system can generate the entity graph 104using the existing table relationship in the database. The entity graph104 can be extracted from the relational database schema and representthe tables as nodes, and relationships between the tables as edgesbetween the nodes. Each node can carry a table in the relationaldatabase 102, except the root node of the entity graph 104 carries amain table where each entry of the table corresponds to one entity whichis a subject of the predictive analytics problem. In an embodimenttherefore, the system can receive information identifying what thetarget variable is before generating the entity graph 104.

A collector 106 can traverse the entity graph 104 starting at the rootnode or main table and traverse the entity graph to a predetermineddepth. In an embodiment, the predetermined depth can be specified ininput received by the system. In other embodiments, the depth can bebased on processing efficiency, or as a function of the processingresources required to traverse the entity graph 104 and collect data. Inan embodiment, the collector 106 can cache intermediate joining tablesto save travel time and memory cost. In an embodiment, the collector 106can also transform paths into a canonical form and check for equivalentpaths to avoid redundant path traversal. If the collector 106 candetermine that an equivalent path may yield similar or redundantinformation, the collector 106 can retain that information to avoidlater traversing the redundant paths in order to save time.

A canonical form is a labeled graph Canon(G) that is isomorphic to G,such that every graph that is isomorphic to G has the same canonicalform as G. Thus, from a solution to the graph canonization problem, onecould also solve the problem of graph isomorphism: to test whether twographs G and H are isomorphic, compute their canonical forms Canon(G)and Canon(H), and test whether these two canonical forms are identical.In this way, by canonizing the paths, the collector 106 can identify theredundant paths.

An extractor 110 can extract features from the collected data bydetermining a type of data collected, selecting an appropriate datamining algorithm based on the determined type of data, and extractingfeatures from the columns of data using the selected data miningalgorithm. The extractor 110 can also receive one or more new extractormodules from a extractor interface 108 that provides for an interfacebetween one or more operators and the system. The extractor interface108 allows operators to provide the system with new data miningalgorithms when new types of data, or data previously unsupported, arecollected. A selector 112 can then apply one or more of the extractedfeatures into the prediction model in order to make a prediction.

Turning now to FIG. 2, illustrated is another block diagram 200 of anexample, non-limiting feature extraction system 202 that in accordancewith one or more embodiments described herein. Repetitive description oflike elements employed in other embodiments described herein is omittedfor sake of brevity.

In FIG. 2, the feature extraction system 202 can include a processor204, a graphing component 206, a joining component 208, and a featureextraction component 210.

In one example, the feature extraction system 202 can be a neuralnetwork (e.g., an artificial neural network, a machine learning neuralnetwork, etc.) associated with interconnected semantic labeling thatprovides an estimated functional model from a set of unknown inputs. Inanother example, the feature extraction system 202 can be associatedwith a Bayesian network that provides a graphical model that representsrelationships between a set of variables (e.g., a set of randomvariables). In yet another example, the feature extraction system 202can be associated with a hidden Markov model that models data over acontinuous time interval and/or outputs a probability distribution.However, the feature extraction system 202 can alternatively beassociated with a different machine learning system such as, but notlimited to, a clustering machine learning system, a decision treemachine learning system, an instance-based machine learning system, aregression machine learning system, a regularization machine learningsystem, rule learning machine learning system, etc. Furthermore, it isto be appreciated that the feature extraction system 202 can be anynumber of different types of machine learning systems to facilitate asemantic labeling process associated with a network of interconnectedprocessing components.

The feature extraction system 202 and/or the components of the featureextraction system 202 can be employed to use hardware and/or software tosolve problems that are highly technical in nature (e.g., related tobioinformatics, authentication, compression, big data analysis etc.),that are not abstract and that cannot be performed as a set of mentalacts by a human. Further, some of the processes performed may beperformed by specialized computers for carrying out defined tasksrelated to the semantic labeling application/subject area. The featureextraction system 202 and/or components of the system can be employed tosolve new problems that arise through advancements in technology,computer networks, the Internet and the like. The feature extractionsystem 202 can provide technical improvements to feature extraction andfeature engineering by improving processing efficiency among processingcomponents in a feature extraction system, reducing delay in processingperformed by processing components in a feature extraction system,avoiding or reducing the likelihood of network bottlenecks betweenprocessing components in a feature extraction system, and/or improvingbandwidth utilization for a network of processing components in afeature extraction system, etc.

A processor 204 can be associated with at least one processor (e.g., acentral processing unit, a graphical processing unit, etc.). In variousembodiments, the processor 204 can be or include hardware, software(e.g., a set of threads, a set of processes, software in execution,etc.) or a combination of hardware and software that performs acomputing task for machine learning (e.g., a machine learning computingtask associated with received data). For example, the processor 204 canexecute data analysis threads that cannot be performed by a human (e.g.,are greater than the capability of a single human mind). For example,the amount of data processed, the speed of processing of the data and/orthe data types processed by processing components 204 over a certainperiod of time can be respectively greater, faster and different thanthe amount, speed and data type that can be processed by a single humanmind over the same period of time. For example, data processed byprocessing components 204 can be raw data (e.g., raw audio data, rawvideo data, raw textual data, raw numerical data, etc.) and/orcompressed data (e.g., compressed audio data, compressed video data,compressed textual data, compressed numerical data, etc.) captured byone or more sensors and/or one or more computing devices. Moreover,processing components 204 can be fully operational towards performingone or more other functions (e.g., fully powered on, fully executed,etc.) while also processing the above-referenced data analysis data andruntime environment data.

In an embodiment, the graphing component 206 can generate a graph (e.g.,entity graph 104) based on a relational database (e.g., relationaldatabase 102), wherein the graph has a set of nodes that correspond torespective tables in the relational database. In another embodiment, thejoining component 208 can join the respective tables to form a joinedtable based on an edge connecting nodes of the set of nodes, wherein thejoined table is joined by a column of data that is shared by therespective tables. In another embodiment, the feature extractioncomponent 210 can extract a feature from the column of data using a datamining algorithm that is selected from a set of data mining algorithmsbased on a type of data in the column of data.

In an embodiment, the graphing component 206 receives as an input, arelational database that has been selected and cleaned by a cleaningalgorithm. The cleaning algorithm can repair and or remove broken andmissing data, as well as standardize the format of the data in therelational database. In one or more embodiments, the cleaning algorithmcan also sample the data set, taking random or pseudo-random samples inorder to reduce the file size of the data set. In other embodiments, thecleaning algorithm can anonymize the data by removing identifying and/orother sensitive information.

In an embodiment, the graphing component 206 can build an entity graphfrom the tables in the relational database by incrementally joining thetables at related columns. For instance if a first table has a set ofcolumns, and a second table has a second set of columns, and a column inthe first set of columns is related to another columns in the second setof columns, the graphing component 206 can make represent the tables asnodes, and connect the nodes along an edge, the edge representing therelationship between the two tables. In an embodiment, each node in theentity graph carries or represents a table in the relational database,and there can be a root node, which corresponds to a main table whereeach of the entry of the main table can correspond to an entity which isa subject of the predictive analytics problem. In an embodiment, thegraphing component 206 can receive input indicating what the targetvariable is, and select the root node based on the desired targetvariable.

In an embodiment, the joining component 208 can then merge the tables atthe related columns. The joining component 208 can determine whichtables and columns are related based on the edge between the nodes inthe entity graph generated by the graphing component 206 and generate acolumn of data that is merged from a column in the first table and acolumn in the second table.

In an embodiment, the feature extraction component 210 can automaticallyextract features from the joined column of data using a data miningalgorithm that is selected based on the type of data in the column ofdata. The data in the column of data can be selected from a groupconsisting spatio-temporal data, time-series data, sequence data, itemset data, number set data, singleton data, text and image data, and etc.The feature extraction component can determine which type of data isincluded, and then select an appropriate data mining algorithm in orderto extract the features and perform pattern discovery. The output of thefeatures can be fed into one or more predictive models. The featureextraction component 210 can also select which of the extracted featuresto submit to the prediction model based on a statistical relevance tothe target variable.

Turning now to FIG. 3, illustrated is another block diagram 300 of anexample, non-limiting feature extraction system 202 that identifies atype of data and selects an appropriate data mining technique inaccordance with one or more embodiments described. herein. Repetitivedescription of like elements employed in other embodiments describedherein is omitted for sake of brevity.

In FIG. 3, the feature extraction component 210 can include anidentification component 302 and a selection component 304, and thefeature extraction system 202 can further include a data miningalgorithm database 306.

In an embodiment, the identification component 302 can determine a typeof data in the joined column of data, and the selection component 304can select the data mining algorithm from the data mining algorithmdatabase 306 based whether the data is from a group consisting ofspatial-temporal data, time-series data, sequence data, item set data,number set data, singleton data, text data and image data.

As an example, if the identification component 302 determines that thecolumn of data contains time series data, the selection component 304can select a signal processing transformation algorithm (e.g., FastFourier Transform or Discrete Wavelet Transform, etc.), anauto-regression algorithm (e.g., autoregressive integrated movingaverage, etc.), outlier detection algorithm, abnormalities detectionalgorithm, or simple statistics algorithm, among other algorithms.

As another example if the identification component 302 determines thatthe column of data contains symbolic sequences data, the selectioncomponent 304 can select a frequent sequential pattern mining algorithm,a predictive sequential pattern mining algorithm, an n-grams algorithmor a simple statistics algorithm, among other algorithms.

As another example, if the identification component 302 determines thatthe column of data contains itemset data, the selection component 304can select a frequent pattern mining algorithm, predictive patternmining algorithm, or a simple statistics algorithm among otheralgorithms. If the column of data is a set of numbers, the selectioncomponent 304 can select a simple statistics algorithm.

As another example, if the identification component 302 determines thatthe column of data contains singleton data and is a categorical value,the selection component 304 can transform the column of data intonumerical features via one-hot coding or a features embedding method.

As another example, if the identification component 302 determines thatthe column of data contains text data, the selection component 304 cantreat the text as symbolic sequence data. In other embodiments, the textmining algorithms can be selected including sentiment analysisalgorithms, topical modeling using Latent Dirichlet Allocationalgorithms, or word embedding algorithms.

As another example, if the identification component 302 determines thatthe column of data contains image data, the selection component 304 canselect a computer vision based feature extraction algorithm, edgedetection algorithm, or object recognition algorithm, among otheralgorithms.

As another example, if the identification component 302 determines thatthe column of data contains spatio-temporal data, the selectioncomponent 304 can select a spatio-clustering algorithm, speed and/oracceleration estimate algorithms, or annotated location algorithms,among other algorithms.

Turning now to FIG. 4, illustrated is an block diagram 400 of example,non-limiting feature extraction system 202 that collects extractedfeatures across a relational database in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity.

In FIG. 4, the feature extraction system 202 can include a collectioncomponent 402 that can collect features extracted from tables bytraversing the graph.

The collection component 402 can traverse the entity graph starting atthe root node or main table and traverse the entity graph to apredetermined depth. In an embodiment, the predetermined depth can bespecified in input received by the system. In other embodiments, thedepth can be based on processing efficiency, or as a function of theprocessing resources required to traverse the entity graph and collectdata. In an embodiment, the collection component 402 can cacheintermediate joining tables to save travel time and memory cost. In anembodiment, the collection component 402 can also transform paths into acanonical form and check for equivalent paths to avoid redundant pathtraversal. If the collection component 402 can determine that anequivalent path may yield similar or redundant information, thecollection component 402 can retain that information to avoid latertraversing the redundant paths in order to save time.

In an embodiment, the collection component 402 can traverse the entitygraph to a depth based on a defined criterion relating to processingefficiency. If the amount of computer resources consumed surpasses apredetermined threshold, the collection component 402 can ceasetraversing the graph. In other embodiments the collection component 402can receive user input indicating a desired depth with which to traversethe entity graph.

Turning now to FIG. 5, illustrated is another block diagram 500 of anexample, non-limiting feature extraction system 202 that interfaces toreceive new data mining algorithms in accordance with one or moreembodiments described herein. Repetitive description of like elementsemployed in other embodiments described herein is omitted for sake ofbrevity.

In FIG. 5, the feature extraction system 202 can include an interfacecomponent 502 that can receive updates to the feature extractioncomponent 210 to allow the feature extraction component 210 to extractfeatures from a new type of data.

In response to identification component 302 determining that a data typein the column of data is unsupported, or does not have a correspondingdata mining algorithm in the data mining algorithm database 306,interface component 502 can receive an updated extraction module thatcan include a new data mining algorithm.

In other embodiments, the interface component 502 can allow users toplug-in their own extractors to deal with new data or features that arenot currently supported by the feature extraction system 202.

While FIG. 2 depicts separate components in the feature extractioncomponent 210, respectively, it is to be appreciated that two or morecomponents can be implemented in a common component in each of FIGS.2-5. Further, it is to be appreciated that the design of the processor204 and/or the graphing component 206, joining component 208, andfeature extraction component 210 can include other component selections,component placements, etc., to facilitate processing for semanticlabeling/or assignment of groups for parallel semantic labeling.Moreover, the aforementioned systems and/or devices have been describedwith respect to interaction between several components. It should beappreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentscan be combined into a single component providing aggregatefunctionality. The components can also interact with one or more othercomponents not specifically described herein for the sake of brevity,but known by those of skill in the art.

Turning now to FIG. 6, illustrated is a flow diagram 600 of an example,non-limiting computer-implemented method that facilitates automaticfeature extraction in accordance with one or more embodiments describedherein. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity.

The method can begin at 902, where the method includes generating, by adevice operatively coupled to a processing unit, an entity graph basedon a relational database, wherein the entity graph comprises a firstnode associated with a first table in the relational database and asecond node associated with a second table in the relational database(e.g., by graphing component 206).

The entity graph can be extracted from the relational database schemaand represent the tables as nodes, and relationships between the tablesas edges between the nodes. Each node can carry a table in therelational database, except the root node of the entity graph carries amain table where each entry of the table corresponds to one entity whichis a subject of the predictive analytics problem. In an embodimenttherefore, the system can receive information identifying what thetarget variable is before generating the entity graph.

The method can continue at 604, where the method includes joining, bythe device, the first table and the second table based on an edgebetween the first table and the second table defined by the entitygraph, wherein a resulting joined table is connected by a column of data(e.g., by joining component 208). The system determine which tables andcolumns are related based on the edge between the nodes in the entitygraph and generate a column of data that is merged from a column in thefirst table and a column in the second table.

The method can continue at 606 where the method includes extracting, bythe device, a feature from the column of data using a data miningalgorithm selected from a set of data mining algorithms based on a typeof data in the column of data (e.g., by feature extraction component210). The data in the column of data can be selected from a groupconsisting spatio-temporal data, time-series data, sequence data, itemset data, number set data, singleton data, text and image data, and etc.The feature extraction method can determine which type of data isincluded, and then select an appropriate data mining algorithm in orderto extract the features and perform pattern discovery. The output of thefeatures can be fed into one or more predictive models. Extractedfeatures can also be selected to submit to the prediction model based ona statistical relevance to the target variable.

Turning now to FIG. 7, illustrated is a flow diagram 700 of anotherexample, non-limiting computer-implemented method that facilitatesautomatic feature extraction in accordance with one or more embodimentsdescribed herein. Repetitive description of like elements employed inother embodiments described herein is omitted for sake of brevity.

The method can begin at 702 where the method includes cleaning, by adevice operatively coupled to a processing unit, a relational databaseby filling in missing values in incomplete data and removing broken data(e.g., by graphing component 206). Cleaning the data can involvestandardizing a format of data in the relational database and filling inmissing values while also removing broken data. In an embodiment,sensitive data that may identify individual entities can be anonymizedIn other embodiments, the data can be sampled in order to reduce thefile size.

The method can continue at 704 where the method includes generating, bythe device, using a relational database, an entity graph with a firstnode and a second node, wherein the first node corresponds to a firsttable and the second node corresponds to a second table, and wherein thefirst table and the second table have respective columns that arerelated to each other.

The method can continue at 706 where the method includes joining, by thedevice, the first table and the second table at the respective columnsthat are related to each other and forming a joined column of data.

The method can continue at 708 where the method includes extracting, bythe device, a feature from the joined column of data using a data miningalgorithm selected from a set of data mining algorithms based on a typeof data in the joined column of data.

For simplicity of explanation, the computer-implemented methodologiesare depicted and described as a series of acts. It is to be understoodand appreciated that the subject innovation is not limited by the actsillustrated and/or by the order of acts, for example acts can occur invarious orders and/or concurrently, and with other acts not presentedand described herein. Furthermore, not all illustrated acts can berequired to implement the computer-implemented methodologies inaccordance with the disclosed subject matter. In addition, those skilledin the art will understand and appreciate that the computer-implementedmethodologies could alternatively be represented as a series ofinterrelated states via a state diagram or events. Additionally, itshould be further appreciated that the computer-implementedmethodologies disclosed hereinafter and throughout this specificationare capable of being stored on an article of manufacture to facilitatetransporting and transferring such computer-implemented methodologies tocomputers. The term article of manufacture, as used herein, is intendedto encompass a computer program accessible from any computer-readabledevice or storage media.

Moreover, because configuration of data packet(s) and/or communicationbetween processing components and/or an assignment component isestablished from a combination of electrical and mechanical componentsand circuitry, a human is unable to replicate or perform the subjectdata packet configuration and/or the subject communication betweenprocessing components and/or an assignment component. For example, ahuman is unable to generate data for transmission over a wired networkand/or a wireless network between processing components and/or anassignment component, etc. Moreover, a human is unable to packetize datathat can include a sequence of bits corresponding to informationgenerated during a machine learning process (e.g., a semantic labelingprocess), transmit data that can include a sequence of bitscorresponding to information generated during a machine learning process(e.g., a semantic labeling process), etc.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 8 as well as the following discussion are intendedto provide a general description of a suitable environment in which thevarious aspects of the disclosed subject matter can be implemented. FIG.8 illustrates a block diagram of an example, non-limiting operatingenvironment in which one or more embodiments described herein can befacilitated. Repetitive description of like elements employed in otherembodiments described herein is omitted for sake of brevity. Withreference to FIG. 8, a suitable operating environment 800 forimplementing various aspects of this disclosure can also include acomputer 812. The computer 812 can also include a processing unit 814, asystem memory 816, and a system bus 818. The system bus 818 couplessystem components including, but not limited to, the system memory 816to the processing unit 814. The processing unit 814 can be any ofvarious available processors. Dual microprocessors and othermultiprocessor architectures also can be employed as the processing unit814. The system bus 818 can be any of several types of bus structure(s)including the memory bus or memory controller, a peripheral bus orexternal bus, and/or a local bus using any variety of available busarchitectures including, but not limited to, Industrial StandardArchitecture (ISA), Micro-Channel Architecture (MSA), Extended ISA(EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB),Peripheral Component Interconnect (PCI), Card Bus, Universal Serial Bus(USB), Advanced Graphics Port (AGP), Firewire (IEEE 1394), and SmallComputer Systems Interface (SCSI). The system memory 816 can alsoinclude volatile memory 820 and nonvolatile memory 822. The basicinput/output system (BIOS), containing the basic routines to transferinformation between elements within the computer 812, such as duringstart-up, is stored in nonvolatile memory 822. By way of illustration,and not limitation, nonvolatile memory 822 can include read only memory(ROM), programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable programmable ROM (EEPROM), flash memory, ornonvolatile random access memory (RAM) (e.g., ferroelectric RAM (FeRAM).Volatile memory 820 can also include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as static RAM (SRAM),dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM(DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), directRambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), and Rambusdynamic RAM.

Computer 812 can also include removable/non-removable,volatile/non-volatile computer storage media. FIG. 8 illustrates, forexample, a disk storage 824. Disk storage 824 can also include, but isnot limited to, devices like a magnetic disk drive, floppy disk drive,tape drive, Jaz drive, Zip drive, LS-100 drive, flash memory card, ormemory stick. The disk storage 824 also can include storage mediaseparately or in combination with other storage media including, but notlimited to, an optical disk drive such as a compact disk ROM device(CD-ROM), CD recordable drive (CD-R Drive), CD rewritable drive (CD-RWDrive) or a digital versatile disk ROM drive (DVD-ROM). To facilitateconnection of the disk storage 824 to the system bus 818, a removable ornon-removable interface is typically used, such as interface 826. FIG. 8also depicts software that acts as an intermediary between users and thebasic computer resources described in the suitable operating environment800. Such software can also include, for example, an operating system828. Operating system 828, which can be stored on disk storage 824, actsto control and allocate resources of the computer 812. Systemapplications 830 take advantage of the management of resources byoperating system 828 through program modules 832 and program data 834,e.g., stored either in system memory 816 or on disk storage 824. It isto be appreciated that this disclosure can be implemented with variousoperating systems or combinations of operating systems. An entity enterscommands or information into the computer 812 through input device(s)836. Input devices 836 include, but are not limited to, a pointingdevice such as a mouse, trackball, stylus, touch pad, keyboard,microphone, joystick, game pad, satellite dish, scanner, TV tuner card,digital camera, digital video camera, web camera, and the like. Theseand other input devices connect to the processing unit 814 through thesystem bus 818 via interface port(s) 838. Interface port(s) 838 include,for example, a serial port, a parallel port, a game port, and auniversal serial bus (USB). Output device(s) 840 use some of the sametype of ports as input device(s) 836. Thus, for example, a USB port canbe used to provide input to computer 812, and to output information fromcomputer 812 to an output device 840. Output adapter 842 is provided toillustrate that there are some output devices 840 like monitors,speakers, and printers, among other output devices 840, which requirespecial adapters. The output adapters 842 include, by way ofillustration and not limitation, video and sound cards that provide ameans of connection between the output device 840 and the system bus818. It should be noted that other devices and/or systems of devicesprovide both input and output capabilities such as remote computer(s)844.

Computer 812 can operate in a networked environment using logicalconnections to one or more remote computers, such as remote computer(s)844. The remote computer(s) 844 can be a computer, a server, a router, anetwork PC, a workstation, a microprocessor based appliance, a peerdevice or other common network node and the like, and typically can alsoinclude many or all of the elements described relative to computer 812.For purposes of brevity, only a memory storage device 846 is illustratedwith remote computer(s) 844. Remote computer(s) 844 is logicallyconnected to computer 812 through a network interface 848 and thenphysically connected via communication connection 850. Network interface848 encompasses wire and/or wireless communication networks such aslocal-area networks (LAN), wide-area networks (WAN), cellular networks,etc. LAN technologies include Fiber Distributed Data Interface (FDDI),Copper Distributed Data Interface (CDDI), Ethernet, Token Ring and thelike. WAN technologies include, but are not limited to, point-to-pointlinks, circuit switching networks like Integrated Services DigitalNetworks (ISDN) and variations thereon, packet switching networks, andDigital Subscriber Lines (DSL). Communication connection(s) 850 refersto the hardware/software employed to connect the network interface 848to the system bus 818. While communication connection 850 is shown forillustrative clarity inside computer 812, it can also be external tocomputer 812. The hardware/software for connection to the networkinterface 848 can also include, for exemplary purposes only, internaland external technologies such as, modems including regular telephonegrade modems, cable modems and DSL modems, ISDN adapters, and Ethernetcards.

Referring now to FIG. 9, an illustrative cloud computing environment 950is depicted. As shown, cloud computing environment 950 includes one ormore cloud computing nodes 910 with which local computing devices usedby cloud consumers, such as, for example, personal digital assistant(PDA) or cellular telephone 1654A, desktop computer 954B, laptopcomputer 954C, and/or automobile computer system 954N may communicate.Nodes 910 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 950 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 954A-Nshown in FIG. 9 are intended to be illustrative only and that computingnodes 910 and cloud computing environment 950 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring now to FIG. 10, a set of functional abstraction layersprovided by cloud computing environment 950 (FIG. 9) is shown. It shouldbe understood in advance that the components, layers, and functionsshown in FIG. 10 are intended to be illustrative only and embodiments ofthe invention are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 1060 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 1061;RISC (Reduced Instruction Set Computer) architecture based servers 1062;servers 1063; blade servers 1064; storage devices 1065; and networks andnetworking components 1066. In some embodiments, software componentsinclude network application server software 1067 and database software1068.

Virtualization layer 1070 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers1071; virtual storage 1072; virtual networks 1073, including virtualprivate networks; virtual applications and operating systems 1074; andvirtual clients 1075.

In one example, management layer 1080 may provide the functionsdescribed below. Resource provisioning 1081 provides dynamic procurementof computing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 1082provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may include applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 1083 provides access to the cloud computing environment forconsumers and system administrators. Service level management 1084provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 1085 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 1090 provides examples of functionality for which thecloud computing environment may be utilized. Non-limiting examples ofworkloads and functions which may be provided from this layer include:mapping and navigation 1091; software development and lifecyclemanagement 1092; virtual classroom education delivery 1093; dataanalytics processing 1094; transaction processing 1095; and transactionmodel software 1096.

The present invention may be a system, a method, an apparatus and/or acomputer program product at any possible technical detail level ofintegration. The computer program product can include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention. The computer readable storage medium can be atangible device that can retain and store instructions for use by aninstruction execution device. The computer readable storage medium canbe, for example, but is not limited to, an electronic storage device, amagnetic storage device, an optical storage device, an electromagneticstorage device, a semiconductor storage device, or any suitablecombination of the foregoing. A non-exhaustive list of more specificexamples of the computer readable storage medium can also include thefollowing: a portable computer diskette, a hard disk, a random accessmemory (RAM), a read-only memory (ROM), an erasable programmableread-only memory (EPROM or Flash memory), a static random access memory(SRAM), a portable compact disc read-only memory (CD-ROM), a digitalversatile disk (DVD), a memory stick, a floppy disk, a mechanicallyencoded device such as punch-cards or raised structures in a groovehaving instructions recorded thereon, and any suitable combination ofthe foregoing. A computer readable storage medium, as used herein, isnot to be construed as being transitory signals per se, such as radiowaves or other freely propagating electromagnetic waves, electromagneticwaves propagating through a waveguide or other transmission media (e.g.,light pulses passing through a fiber-optic cable), or electrical signalstransmitted through a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network can comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device. Computer readable programinstructions for carrying out operations of the present invention can beassembler instructions, instruction-set-architecture (ISA) instructions,machine instructions, machine dependent instructions, microcode,firmware instructions, state-setting data, configuration data forintegrated circuitry, or either source code or object code written inany combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions can execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer can beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection can be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) can execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions. These computer readable programinstructions can be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks. These computer readable program instructions can also be storedin a computer readable storage medium that can direct a computer, aprogrammable data processing apparatus, and/or other devices to functionin a particular manner, such that the computer readable storage mediumhaving instructions stored therein comprises an article of manufactureincluding instructions which implement aspects of the function/actspecified in the flowchart and/or block diagram block or blocks. Thecomputer readable program instructions can also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational acts to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams can represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the blocks can occur out of theorder noted in the Figures. For example, two blocks shown in successioncan, in fact, be executed substantially concurrently, or the blocks cansometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

While the subject matter has been described above in the general contextof computer-executable instructions of a computer program product thatruns on a computer and/or computers, those skilled in the art willrecognize that this disclosure also can or can be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that theinventive computer-implemented methods can be practiced with othercomputer system configurations, including single-processor ormultiprocessor computer systems, mini-computing devices, mainframecomputers, as well as computers, hand-held computing devices (e.g., PDA,phone), microprocessor-based or programmable consumer or industrialelectronics, and the like. The illustrated aspects can also be practicedin distributed computing environments where tasks are performed byremote processing devices that are linked through a communicationsnetwork. However, some, if not all aspects of this disclosure can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules can be located in both local and remotememory storage devices.

As used in this application, the terms “component,” “system,”“platform,” “interface,” and the like, can refer to and/or can include acomputer-related entity or an entity related to an operational machinewith one or more specific functionalities. The entities disclosed hereincan be either hardware, a combination of hardware and software,software, or software in execution. For example, a component can be, butis not limited to being, a process running on a processor, a processor,an object, an executable, a thread of execution, a program, and/or acomputer. By way of illustration, both an application running on aserver and the server can be a component. One or more components canreside within a process and/or thread of execution and a component canbe localized on one computer and/or distributed between two or morecomputers. In another example, respective components can execute fromvarious computer readable media having various data structures storedthereon. The components can communicate via local and/or remoteprocesses such as in accordance with a signal having one or more datapackets (e.g., data from one component interacting with anothercomponent in a local system, distributed system, and/or across a networksuch as the Internet with other systems via the signal). As anotherexample, a component can be an apparatus with specific functionalityprovided by mechanical parts operated by electric or electroniccircuitry, which is operated by a software or firmware applicationexecuted by a processor. In such a case, the processor can be internalor external to the apparatus and can execute at least a part of thesoftware or firmware application. As yet another example, a componentcan be an apparatus that provides specific functionality throughelectronic components without mechanical parts, wherein the electroniccomponents can include a processor or other means to execute software orfirmware that confers at least in part the functionality of theelectronic components. In an aspect, a component can emulate anelectronic component via a virtual machine, e.g., within a cloudcomputing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form. As used herein, the terms “example”and/or “exemplary” are utilized to mean serving as an example, instance,or illustration. For the avoidance of doubt, the subject matterdisclosed herein is not limited by such examples. In addition, anyaspect or design described herein as an “example” and/or “exemplary” isnot necessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent structuresand techniques known to those of ordinary skill in the art.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. Additionally, a processor can refer to an integratedcircuit, an application specific integrated circuit (ASIC), a digitalsignal processor (DSP), a field programmable gate array (FPGA), aprogrammable logic controller (PLC), a complex programmable logic device(CPLD), a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor can also beimplemented as a combination of computing processing units. In thisdisclosure, terms such as “store,” “storage,” “data store,” “datastorage,” “database,” and substantially any other information storagecomponent relevant to operation and functionality of a component areutilized to refer to “memory components,” entities embodied in a“memory,” or components comprising a memory. It is to be appreciatedthat memory and/or memory components described herein can be eithervolatile memory or nonvolatile memory, or can include both volatile andnonvolatile memory. By way of illustration, and not limitation,nonvolatile memory can include read only memory (ROM), programmable ROM(PROM), electrically programmable ROM (EPROM), electrically erasable ROM(EEPROM), flash memory, or nonvolatile random access memory (RAM) (e.g.,ferroelectric RAM (FeRAM). Volatile memory can include RAM, which canact as external cache memory, for example. By way of illustration andnot limitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM),direct Rambus RAM (DRRAM), direct Rambus dynamic RAM (DRDRAM), andRambus dynamic RAM (RDRAM). Additionally, the disclosed memorycomponents of systems or computer-implemented methods herein areintended to include, without being limited to including, these and anyother suitable types of memory.

What has been described above include mere examples of systems andcomputer-implemented methods. It is, of course, not possible to describeevery conceivable combination of components or computer-implementedmethods for purposes of describing this disclosure, but one of ordinaryskill in the art can recognize that many further combinations andpermutations of this disclosure are possible. Furthermore, to the extentthat the terms “includes,” “has,” “possesses,” and the like are used inthe detailed description, claims, appendices and drawings such terms areintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim. The descriptions of the various embodiments have been presentedfor purposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

What is claimed is:
 1. A system, comprising: a memory that storescomputer executable components; and a processor that executes thecomputer executable components stored in the memory, wherein thecomputer executable component comprise: a graphing component that cangenerate a graph based on a relational database, wherein the graph has aset of nodes that correspond to respective tables in the relationaldatabase; a joining component that can join the respective tables toform a joined table based on an edge connecting nodes of the set ofnodes, wherein the joined table is joined by a column of data that isshared by the respective tables; and a feature extraction component thatcan extract a feature from the column of data using a data miningalgorithm that is selected from a set of data mining algorithms based ona type of data in the column of data.
 2. The system of claim 1, whereinthe feature extraction component further comprises: an identificationcomponent that can determine the type of data in the column of data; anda selection component that can select the data mining algorithm basedwhether the data is from a group consisting of spatial-temporal data,time-series data, sequence data, item set data, number set data,singleton data, text data and image data.
 3. The system of claim 2,wherein the selection component can select at least one algorithm from agroup consisting of a signal processing transformation algorithm, anauto-regression algorithm, an outlier detection algorithm, anabnormalities detection algorithm and a statistics algorithm in responseto the identification component determining the type of data istime-series data.
 4. The system of claim 2, wherein the selectioncomponent can select at least one algorithm from a group consisting of afrequent sequential pattern mining algorithm, a predictive sequentialpattern mining algorithm, an n-grams model algorithm, and a statisticsalgorithm in response to the identification component determining thetype of data is sequence data.
 5. The system of claim 2, wherein theselection component can select at least one algorithm from a groupconsisting of a frequent pattern mining algorithm, a predictive patternmining algorithm, and a statistics algorithm in response to theidentification component determining the type of data is item set data.6. The system of claim 1, further comprising: a collection componentthat can collect features extracted from tables by traversing the graph.7. The system of claim 6, wherein the collection component can traversethe graph to a depth based on a defined criterion.
 8. The system ofclaim 1 further comprising: an interface component that can receiveupdates to the feature extraction component to allow the featureextraction component to extract features from a new type of data.
 9. Asystem, comprising: a memory that stores computer executableinstructions, a processor that executes that computer executableinstructions to perform operations, comprising: generating an entitygraph based on a relational database, wherein the entity graph comprisesa first node associated with a first table in the relational databaseand a second node associated with a second table in the relationaldatabase; joining the first table and the second table based on an edgebetween the first table and the second table defined by the entitygraph, wherein a resulting joined table is connected by a column ofdata; determining a type of the data in the column of data; selecting adata mining algorithm from a set of data mining algorithms based on thetype of data in the column of data; and extracting a feature from thecolumn of data using the data mining algorithm.
 10. The system of claim9, wherein the operations further comprise: collecting featuresextracted from tables by traversing the entity graph to a depthdetermined based on a defined criterion.
 11. A computer program productto provide automatic feature extraction, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processing component to cause the processing component to: generate anentity graph based on a relational database, wherein the entity graphcomprises a first node associated with a first table in the relationaldatabase and a second node associated with a second table in therelational database; receive a target variable based on a user input;join the first table and the second table based on an edge between thefirst table and the second table defined by the entity graph, wherein aresulting joined table is connected by a column of data and the columnof data is associated with the target variable; and extract a featurefrom the column of data using a data mining algorithm selected from aset of data mining algorithms based on a type of data in the column ofdata.
 12. The computer program product of claim 11, wherein the entitygraph is traversed to a depth based on a defined criterion related toprocessing efficiency.
 13. The computer program product of claim 11,wherein the entity graph is traversed to a depth based on a definedcriterion related to the user input.